• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.822-828
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• 2002

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. However, the use have been confined because of OF estimation time as well as error problem. This paper shows a new method form effectively extracting movement information using the multi-constraint base approaches with sobel edge detection. The moving objects anr extraced in the input image sequence using edge detection and segmentation. Edge detection and difference of the two input image sequence gives us the moving objects in the images. The process of thresholding removes the moving objects detected due to noise. After thresholding the real moving objects, we applied the Combinatorial Hough Transform (CHT) and voting accumulation to find the optimal constraint lines for optical flow estimation. The moving objects found in the two consecutive images by using edge detection and segmentation greatly reduces the time for comutation of CHT. The voting based CHT avoids the errors associated with least squares methods. Calculation of a large number of points along the constraint line is also avoided by using the transformed slope-intercept parameter domain. The simulation results show that the proposed method is very effective for extracting optical flow vectors and hence recognizing moving objects in the images.

• Smart Structures and Systems
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• v.28 no.6
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• pp.779-789
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• 2021

Cracks in railway sleeper are an inevitable condition and has a significant influence on the safety of railway system. Although the technology of railway sleeper condition monitoring using machine learning (ML) models has been widely applied, the crack recognition accuracy is still in need of improvement. In this paper, a two-stage method using edge detection and convolutional neural network (CNN) is proposed to reduce the burden of computing for detecting cracks in railway sleepers with high accuracy. In the first stage, the edge detection is carried out by using the 3×3 neighborhood range algorithm to find out the possible crack areas, and a series of mathematical morphology operations are further used to eliminate the influence of noise targets to the edge detection results. In the second stage, a CNN model is employed to classify the results of edge detection. Through the analysis of abundant images of sleepers with cracks, it is proved that the cracks detected by the neighborhood range algorithm are superior to those detected by Sobel and Canny algorithms, which can be classified by proposed CNN model with high accuracy.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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• pp.97-101
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• 1987

The edge, a discontinuity or abrupt change in the gray-level or color, is a fundamentally important primitive feature of an image necessary for the image analysis and classification. Two-dimensional 3x3 compass gradient operators (ex. Sobel, Prewitt, and Kirsch operators)are commonly used in the edge detection and usually detect 8 compass directional components. In this paper, we present a new interpretation of the relationships between the resulting 8 gradient magnitudes and the 8 intensity values of neighboring pixels which are covered by the two-dimensional 3x3 mask. It is expected that a new gradient edge operator may be designed by changing the eigenvalues in the transform domain and the fast optical edge operator may be implemented by using the optical system.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.474-486
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• 2010

In this paper, we propose a new method for detection moving object contour using spatial and temporal edge. In general, contour pixels of the moving object are likely present around pixels with high gradient value along the time axis and the spatial axis. Therefore, we can detect the contour of the moving objects by finding pixels which have high gradient value in the time axis and spatial axis. In this paper, we introduce a new computation method, termed as temporal edge, to compute an gradient value along the time axis for any pixel on an image. The temporal edge can be computed using two input gray images at time t and t-2 using the Sobel operator. Temporal edge is utilized to detect a candidate region of the moving object contour and then the detected candidate region is used to extract spatial edge information. The final contour of the moving object is detected using the combination of these two edge information, which are temporal edge and spatial edge, and then the post processing such as a morphological operation and a background edge removing procedure are applied to remove noise regions. The complexity of the proposed method is very low because it dose not use any background scene and high complex operation, therefore it can be applied to real-time applications. Experimental results show that the proposed method outperforms the conventional contour extraction methods in term of processing effort and a ghost effect which is occurred in the case of entropy method.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.78-88
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• 2003

A novel adaptive error concealment technique is proposed using a variable operating region algorithm based on MPEG-4 coding. In the algorithm, a missing block is classified as flat or edge block based on local information from the surrounding blocks extracted using a Sobel operation in a variable operating region (VOR). In this case, the VOR is determined adaptively according to the number of edge directions in the missing block. 1;sing the classification, the flat blocks are then concealed by the Proposed mean based weighted bilinear interpolation (MWBLI) method, and the edge blocks by the boundary directional interpolation (BDI) method. Consequently, the use of the Proposed VOR improves the subjective performance in a curved edge region, while the adaptive processing based on block classification improves the objective performance. Experimental results confirmed that the proposed algorithm produced better results than conventional algorithms, both subjectively and objectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.4
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• pp.20-28
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• 2000

The error diffusion algorithm is good for reproducing continuous image to binary image. However the reproduction of edge characteristic is weak in power spectrum analysts of display error. In this paper, an error diffusion method which include pre-filter algorithm for edge characteristic enhancement is proposed Pre-filter algorithm is organized horizontal and vertical directional differential value and weighting function of pre-filter First, it is obtained the horizontal and vertical differential value from the peripheral pixels in original image using $3{\times}3$ Sobel operator Secondly weighting function of pre-filter is composed by function including absolute value and sign of differential value The improved Error diffusion algorithm using pre-filter, present a good result visually which edge characteristic is enhanced. The difference between orignal image and halftoning image is compared with edge-enhanced error diffusion algorithm by measuring the radially averaged power spectrum density.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.4
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• pp.135-140
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• 2014

As the digital image devices are widely used, interests in the software- and the hardware-related image processing become higher and the image processing techniques are applied in various fields such as object recognition, object detection, fingerprint recognition, and etc. For the edge detections Sobel, Prewitt, Laplacian, Roberts and Canny detectors are used and these existing methods can excellently detect the edges of the images without noise. However, in the images corrupted by the impulse noise, these methods are insufficent in noise elimination characteristics, showing unsatisfactory edge detection. Therefore in this paper, in order to obtain excellent edge detection characteristics in the corrupted image by the impulse noise, an detection algorithm is porposed, which uses the central pixel of mask divided by four regions along the axis, calculates the estimated mask according to the representing pixel values in each regions, and detects the final edges by applying the estimates mask and the new directional one.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1405-1408
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• 1987

X-ray filmed data converted by Image digitizer were processed through the digital computer for edge detections. Sobel operator with the high-pass and the low-pass filtering of the histogram of the images were applied to this work and 3 examples were processed.

• Proceedings of the IEEK Conference
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• 2000.11b
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• pp.112-115
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• 2000

This paper describes the algorithm, architecture and design of the circuit implementing motion tracing features based on the edge detection. The Sobel operation was used to compute the edges of moving objects. Motion tracing is performed by searching for the center of the edges for each frame and adding those centers. The edger and the centers of the moving object from camera were displayed in the monitor and verified using Xillinx FPGA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.283-286
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• 2001

Based on a fuzzy system representation of gray scale images, we derive an edge detection algorithm whose convolution kernel is different from the known kernels such as those of Roberts', Prewitt's or Sobel's gradient. Our fuzzy system representation is an exact representation of the bicubic spline function which represents the gray scale image approximately. Hence the fuzzy system is a continuous function and it provides a natural way to define the gradient and the Laplacian operator. We show that the gradient at grid points can be evaluated by taking the convolution of the image with a 3 3 kernel. We also show that our gradient coupled with the approximate value of the continuous function generates an edge detection method which creates edge images clearer than those by other methods. A few examples of applying our methods are included.